Omics data tables evaluation report

1 Introduction

In this evaluation, there are total 6 data tables. We used the evaluation metrics implemented in OmicsEV package to evaluate these data tables. The sample and class information for each data table are shown in the table below.

class	d1	d2	d3	d4	d5	d6
Basal	17	17	17	17	17	17
Her2	12	12	12	12	12	12
LumA	19	19	19	19	19	19
LumB	22	22	22	22	22	22
None	16	16	16	16	16	16

The detailed sample information is shown below.

sample	class	batch	order
TCGA.A2.A0CM	Basal	1	1
TCGA.A2.A0D0	Basal	1	2
TCGA.A2.A0D1	None	1	3
TCGA.A2.A0D2	Basal	1	4
TCGA.A2.A0EQ	Her2	1	5
TCGA.A2.A0EV	LumA	1	6
TCGA.A2.A0EX	LumA	1	7
TCGA.A2.A0EY	LumB	1	8
TCGA.A2.A0SW	LumB	1	9
TCGA.A2.A0SX	Basal	1	10
TCGA.A2.A0T1	Her2	1	11
TCGA.A2.A0T2	Basal	1	12
TCGA.A2.A0T6	LumA	1	13
TCGA.A2.A0T7	LumA	1	14
TCGA.A2.A0YC	LumA	1	15
TCGA.A2.A0YD	LumA	1	16
TCGA.A2.A0YF	LumA	1	17
TCGA.A2.A0YG	LumB	1	18
TCGA.A2.A0YI	LumA	1	19
TCGA.A2.A0YL	LumA	1	20
TCGA.A2.A0YM	Basal	1	21
TCGA.A7.A0CD	LumA	1	22
TCGA.A7.A0CE	Basal	1	23
TCGA.A7.A0CJ	LumB	1	24
TCGA.A8.A06N	LumB	1	25
TCGA.A8.A06Z	LumB	1	26
TCGA.A8.A076	LumB	1	27
TCGA.A8.A079	LumB	1	28
TCGA.A8.A09G	Her2	1	29
TCGA.A8.A09I	LumB	1	30
TCGA.AN.A04A	None	1	31
TCGA.AN.A0AJ	LumB	1	32
TCGA.AN.A0AL	Basal	1	33
TCGA.AN.A0AM	LumB	1	34
TCGA.AN.A0AS	LumA	1	35
TCGA.AN.A0FK	LumA	1	36
TCGA.AN.A0FL	Basal	1	37
TCGA.AO.A03O	None	1	38
TCGA.AO.A0J6	None	1	39
TCGA.AO.A0J9	None	1	40
TCGA.AO.A0JC	None	1	41
TCGA.AO.A0JE	None	1	42
TCGA.AO.A0JJ	None	1	43
TCGA.AO.A0JL	None	1	44
TCGA.AO.A0JM	None	1	45
TCGA.AO.A126	None	1	46
TCGA.AO.A12B	None	1	47
TCGA.AO.A12E	None	1	48
TCGA.AR.A0TR	LumA	1	49
TCGA.AR.A0TT	LumB	1	50
TCGA.AR.A0TV	LumB	1	51
TCGA.AR.A0TX	Her2	1	52
TCGA.AR.A0U4	None	1	53
TCGA.BH.A0EE	Her2	1	54
TCGA.BH.A0HP	LumA	1	55
TCGA.A2.A0T3	LumB	2	56
TCGA.A7.A13F	LumB	2	57
TCGA.AO.A12D	None	2	58
TCGA.AO.A12F	None	2	59
TCGA.AR.A0TY	LumB	2	60
TCGA.AR.A1AQ	Basal	2	61
TCGA.AR.A1AV	LumA	2	62
TCGA.AR.A1AW	LumB	2	63
TCGA.BH.A0AV	Basal	2	64
TCGA.BH.A0C1	LumA	2	65
TCGA.BH.A0C7	LumB	2	66
TCGA.BH.A0E9	LumA	2	67
TCGA.C8.A12L	Her2	2	68
TCGA.C8.A12P	Her2	2	69
TCGA.C8.A12Q	Her2	2	70
TCGA.C8.A12T	Her2	2	71
TCGA.C8.A12U	LumB	2	72
TCGA.C8.A12V	Basal	2	73
TCGA.C8.A12W	LumB	2	74
TCGA.C8.A12Z	Her2	2	75
TCGA.C8.A130	LumB	2	76
TCGA.C8.A131	Basal	2	77
TCGA.C8.A134	Basal	2	78
TCGA.C8.A135	Her2	2	79
TCGA.C8.A138	Her2	2	80
TCGA.D8.A13Y	LumB	2	81
TCGA.D8.A142	Basal	2	82
TCGA.E2.A10A	LumA	2	83
TCGA.E2.A150	Basal	2	84
TCGA.E2.A154	LumA	2	85
TCGA.E2.A159	Basal	2	86

2 Overview

The table below provides an overview about all the quantitative metrics generated in the evaluation. For each metric, the value of the best data table is highlighted. The detail of each metric can be found in corresponding section below.

metric	d1	d2	d3	d4	d5	d6
#identified features	18845 (0.9244)	18845 (0.9244)	18845 (0.9244)	18845 (0.9244)	18845 (0.9244)	18845 (0.9244)
#quantifiable features	17416 (0.8543)	17416 (0.8543)	17416 (0.8543)	17416 (0.8543)	17416 (0.8543)	17416 (0.8543)
non_missing_value_ratio	0.9780	0.9780	0.9780	0.9780	0.9780	0.9780
data_dist_similarity	0.9739	1.0000	0.9864	0.9863	0.9621	0.9766
silhouette_width	0.0145 (0.9855)	-0.0009 (0.9991)	0.0139 (0.9861)	0.0144 (0.9856)	0.0200 (0.9800)	0.0214 (0.9786)
pcRegscale	0.1682 (0.8318)	0.2771 (0.7229)	0.1720 (0.8280)	0.1731 (0.8269)	0.0954 (0.9046)	0.0000 (1.0000)
complex_auc	0.6520	0.6340	0.6815	0.6819	0.6320	0.6536
func_auc	0.7758	0.7740	0.7871	0.7984	0.8072	0.7918
class_auc	0.9900	0.9943	0.9887	0.9898	0.9913	0.9940
gene_wise_cor	0.3294	0.3354	0.3374	0.3384	0.3207	0.3283
sample_wise_cor	0.1421	0.1421	0.1421	0.1421	0.1421	0.1383

The radar plot showing below is generated based on the data in the above overview table. To generate the radar plot, a metric is converted to a scale in which the value range is between 0 and 1 in a way that higher value indicates better data quality if necessary. The converted values are in parentheses.

3 Data depth

3.1 Study-wise

The table below shows the number of identified proteins or genes for each data table. We take the proteins or genes filtered by 50% missing value as quantified proteins or genes. The values in parentheses are the percentage of proteins or genes identified or quantified based on the total number of proteins or genes (20386) in the study species.

data table	#identified features	#quantifiable features
d1	18845 (92.44%)	17416 (85.43%)
d2	18845 (92.44%)	17416 (85.43%)
d3	18845 (92.44%)	17416 (85.43%)
d4	18845 (92.44%)	17416 (85.43%)
d5	18845 (92.44%)	17416 (85.43%)
d6	18845 (92.44%)	17416 (85.43%)

Upset chart below showing overlap in proteins or genes identified in each data table. Numbers of identified proteins or genes shared between different data tables are indicated in the top bar chart and the specific data tables in each set are indicated with solid points below the bar chart. Total identifications for each data table are indicated on the left as ‘Set size’.

3.2 Sample-wise

The figures below show the number of proteins or genes identified in each sample. Only when the quantification value of a gene or protein is not “NA” in a sample, this gene or protein is considered as identified in the sample. The samples from different batches are coded in different shapes and the samples from different classes are coded in different colors.

d1d2d3d4d5d6

3.3 Missing value distribution

The missing value distribution can give an overview of the percent of missing values of all proteins or genes in both the QC and experiment samples.

data table	non_missing_value_ratio
d1	0.978
d2	0.978
d3	0.978
d4	0.978
d5	0.978
d6	0.978

d1d2d3d4d5d6

4 Data normalization

4.1 Boxplot

The boxplots show the protein or gene expression distribution across samples. X axis is sample ordered by input order. Y axis is log2 transformed protein or gene expression. The samples from different classes are coded in different colors.

d1d2d3d4d5d6

To quantify the normalization effect, for each pair of samples, perform an AUROC test to quantify the ability of feature abundance to distinguish the two samples and then generate a score based on 1-2*abs(AUROC-0.5), which will be 0 to 1, higher the better (no systematic difference between the two samples). The final metric for each data table is the median of scores from all sample pairs.

data table	data_dist_similarity	n
d1	0.9739	3655
d2	1.0000	3655
d3	0.9864	3655
d4	0.9863	3655
d5	0.9621	3655
d6	0.9766	3655

4.2 Density plot

The density plots show the protein or gene expression distribution across samples. X axis is log2 transformed protein or gene expression. Y axis is density.

5 Batch effect

5.1 Silhouette width

The silhouette width s(i) ranges from –1 to 1, with s(i) -> 1 if two clusters are separate and s(i) -> −1 if two clusters overlap but have dissimilar variance. If s(i) -> 0, both clusters have roughly the same structure. Thus, we use the absolute value |s| as an indicator for the presence or absence of batch effects.

data table	silhouette_width
d1	0.0145
d2	-0.0009
d3	0.0139
d4	0.0144
d5	0.0200
d6	0.0214

5.2 PCA with batch annotation

For each PC, we calculate Pearson’s correlation coefficient with batch covariate b:

r_i =corr(PC_i,b)

In a linear model with a single dependent, as is the case here for the PCs correlated to batch covariate, the coefficient of determination R² is the squared Pearson’s correlation coefficient:

R²(PC_i,b) = r_i²

Then we estimate the significance of the correlation coefficient either with a t-test or a one-way ANOVA. The R² value highlighted with red is significant (p-value <= 0.05).

PC	d1	d2	d3	d4	d5	d6
1	0.007	0.012	0.01	0.01	0	0.008
2	0.015	0	0.001	0.001	0.041	0.001
3	0.048	0.092	0.013	0.013	0.027	0.006
4	0.1	0.118	0.11	0.11	0.116	0.026
5	0	0.002	0.002	0.002	0.003	0.001
6	0.093	0.007	0.121	0.119	0.076	0.005
7	0.006	0.038	0.005	0.003	0.003	0.006
8	0.01	0.228	0.035	0.038	0.011	0
9	0.033	0.028	0.029	0.027	0.025	0.005
10	0.001	0.002	0.004	0.003	0.015	0.001

The fraction of variance explained for each PC:

PC	d1	d2	d3	d4	d5	d6
1	10.8	10.8	10.9	11.0	11.2	11.0
2	7.9	6.7	7.3	7.3	9.1	7.8
3	5.4	5.1	4.9	4.9	5.7	5.3
4	4.5	4.7	4.6	4.6	4.4	4.3
5	4.1	4.0	4.0	4.0	3.9	4.2
6	3.0	2.9	2.9	3.0	3.1	2.9
7	2.8	2.7	2.8	2.9	2.7	2.8
8	2.2	2.2	2.2	2.2	2.1	2.2
9	2.0	2.1	2.1	2.1	2.0	2.0
10	1.9	2.1	1.9	1.9	1.9	1.9

‘Scaled PC regression’, i.e. total variance of PCs which correlate significantly with batch covariate (FDR<0.05) scaled by the total variance of 10 PCs:

data table	pcRegscale
d1	0.1682
d2	0.2771
d3	0.1720
d4	0.1731
d5	0.0954
d6	0.0000

In these figures, each column is a sample, each row is also a sample. The color indicates the correlation between samples. The samples are ordered by batches.

5.3 Correlation heatmap

In these figures, each column is a sample, each row is also a sample. The color indicates the correlation between samples. The samples are ordered by batches.

d1d2d3d4d5d6

6 Biological signal

6.1 Correlation among protein complex members

The table showing below is a summary of the evaluation. ‘diff’ is Cor(intra) - Cor(inter). ‘complex_auc’ is the AUROC value based on correlation of protein pairs from different groups.

data table	InterComplex	IntraComplex	diff	complex_auc
d1	0.0413	0.1634	0.1220	0.6520
d2	0.0050	0.1064	0.1013	0.6340
d3	0.0219	0.1715	0.1496	0.6815
d4	0.0201	0.1697	0.1496	0.6819
d5	0.0712	0.1825	0.1112	0.6320
d6	0.0410	0.1621	0.1210	0.6536
Protein	0.0047	0.1722	0.1676	0.6426

6.2 Gene function prediction

In this evaluation, each data table was used to build co-expression network. For a selected network and a selected function term (such as GO or KEGG), proteins/genes annotated to the term and also included in the network were defined as a positive protein/gene set and other proteins/genes in the network constituted the negative protein/gene set for the term. For a selected function term, we use some of the proteins/genes as the seed protein/gene, then we use random walk algorithm to calculate scores for other proteins/genes. A higher score of a protein/gene represents a closer relationship between the protein/gene and the seed proteins/genes. Finally, for each selected function term, we calculate an AUROC to evaluate the prediction performance.

	d1	d2	d3	d4	d5	d6	Protein
Acute myeloid leukemia	0.543	0.587	0.641	0.595	0.546	0.543	0.588
Adherens junction	0.619	0.627	0.584	0.542	0.615	0.551	0.606
Adipocytokine signaling pathway	0.651	0.577	0.584	0.582	0.595	0.62	0.62
Alanine, aspartate and glutamate metabolism	0.651	0.58	0.573	0.6	0.702	0.658	0.679
Aldosterone-regulated sodium reabsorption	0.658	0.683	0.535	0.628	0.622	0.606	0.646
Allograft rejection	1	1	1	1	1	1	0.916
Alzheimers disease	0.708	0.646	0.699	0.72	0.724	0.684	0.733
Amino sugar and nucleotide sugar metabolism	0.645	0.641	0.604	0.601	0.66	0.608	0.638
Aminoacyl-tRNA biosynthesis	0.75	0.749	0.778	0.766	0.687	0.739	0.627
Amoebiasis	0.598	0.635	0.598	0.591	0.629	0.597	0.779
Amyotrophic lateral sclerosis (ALS)	0.661	0.607	0.7	0.652	0.648	0.609	0.58
Antigen processing and presentation	0.858	0.803	0.86	0.865	0.773	0.782	0.573
Apoptosis	0.615	0.596	0.584	0.563	0.559	0.63	0.563
Arachidonic acid metabolism	0.563	0.589	0.635	0.662	0.714	0.677	0.58
Arginine and proline metabolism	0.641	0.67	0.586	0.6	0.587	0.606	0.611
Arrhythmogenic right ventricular cardiomyopathy (ARVC)	0.637	0.63	0.638	0.592	0.625	0.676	0.693
Autoimmune thyroid disease	1	0.999	1	1	1	1	0.904
Axon guidance	0.667	0.629	0.604	0.59	0.633	0.615	0.56
B cell receptor signaling pathway	0.599	0.58	0.522	0.568	0.526	0.624	0.674
Bacterial invasion of epithelial cells	0.615	0.556	0.548	0.556	0.564	0.574	0.767
Basal transcription factors	0.627	0.487	0.519	0.522	0.588	0.556	0.669
Base excision repair	0.668	0.619	0.612	0.625	0.691	0.721	0.757
beta-Alanine metabolism	0.591	0.586	0.687	0.587	0.682	0.658	0.69
Bile secretion	0.609	0.593	0.594	0.602	0.684	0.587	0.646
Biosynthesis of unsaturated fatty acids	0.679	0.679	0.552	0.735	0.651	0.6	0.83
Bladder cancer	0.621	0.635	0.57	0.542	0.61	0.633	0.621
Butanoate metabolism	0.722	0.626	0.619	0.647	0.648	0.624	0.736
Calcium signaling pathway	0.609	0.535	0.585	0.613	0.606	0.6	0.61
Carbohydrate digestion and absorption	0.637	0.683	0.686	0.661	0.689	0.617	0.761
Cardiac muscle contraction	0.704	0.654	0.688	0.703	0.683	0.72	0.878
Cell adhesion molecules (CAMs)	0.796	0.786	0.788	0.815	0.812	0.795	0.77
Cell cycle	0.737	0.728	0.742	0.729	0.692	0.732	0.747
Chagas disease (American trypanosomiasis)	0.594	0.6	0.63	0.571	0.542	0.566	0.581
Chemokine signaling pathway	0.576	0.561	0.564	0.561	0.61	0.535	0.715
Citrate cycle (TCA cycle)	0.637	0.768	0.769	0.739	0.643	0.658	0.866
Collecting duct acid secretion	0.688	0.733	0.674	0.658	0.626	0.688	0.722
Colorectal cancer	0.547	0.528	0.576	0.573	0.589	0.596	0.646
Complement and coagulation cascades	0.867	0.842	0.78	0.867	0.838	0.874	0.907
Cysteine and methionine metabolism	0.61	0.615	0.593	0.653	0.705	0.712	0.622
Cytokine-cytokine receptor interaction	0.701	0.626	0.772	0.756	0.776	0.739	0.639
Cytosolic DNA-sensing pathway	0.65	0.617	0.687	0.727	0.673	0.702	0.583
Dilated cardiomyopathy	0.613	0.592	0.625	0.605	0.677	0.623	0.699
DNA replication	0.723	0.758	0.787	0.775	0.843	0.8	0.866
Drug metabolism - cytochrome P450	0.762	0.747	0.68	0.743	0.776	0.731	0.658
Drug metabolism - other enzymes	0.63	0.67	0.616	0.672	0.631	0.642	0.662
ECM-receptor interaction	0.857	0.851	0.82	0.816	0.883	0.866	0.916
Endocytosis	0.564	0.563	0.568	0.585	0.602	0.59	0.601
Endometrial cancer	0.593	0.539	0.596	0.551	0.57	0.531	0.631
Epithelial cell signaling in Helicobacter pylori infection	0.549	0.565	0.562	0.541	0.568	0.533	0.706
ErbB signaling pathway	0.579	0.6	0.62	0.591	0.576	0.553	0.535
Ether lipid metabolism	0.685	0.7	0.675	0.66	0.806	0.71	0.61
Fatty acid elongation in mitochondria	0.69	0.535	0.701	0.671	0.68	0.633	0.637
Fatty acid metabolism	0.652	0.642	0.6	0.628	0.684	0.543	0.717
Fc epsilon RI signaling pathway	0.555	0.564	0.636	0.533	0.567	0.616	0.68
Fc gamma R-mediated phagocytosis	0.584	0.671	0.65	0.659	0.61	0.638	0.74
Focal adhesion	0.669	0.632	0.666	0.671	0.672	0.686	0.75
Fructose and mannose metabolism	0.641	0.612	0.582	0.659	0.622	0.652	0.651
Galactose metabolism	0.623	0.605	0.642	0.599	0.639	0.554	0.641
Gap junction	0.595	0.584	0.565	0.619	0.551	0.622	0.706
Gastric acid secretion	0.596	0.686	0.572	0.635	0.608	0.579	0.618
Glioma	0.587	0.629	0.559	0.601	0.599	0.624	0.536
Glutathione metabolism	0.614	0.641	0.648	0.608	0.632	0.638	0.637
Glycerolipid metabolism	0.593	0.546	0.545	0.605	0.572	0.653	0.637
Glycerophospholipid metabolism	0.596	0.554	0.592	0.562	0.636	0.617	0.619
Glycine, serine and threonine metabolism	0.551	0.563	0.63	0.628	0.688	0.739	0.583
Glycolysis / Gluconeogenesis	0.602	0.565	0.622	0.637	0.595	0.624	0.743
Glyoxylate and dicarboxylate metabolism	0.797	0.677	0.648	0.698	0.694	0.641	0.677
GnRH signaling pathway	0.628	0.685	0.705	0.671	0.678	0.669	0.578
Graft-versus-host disease	1	1	1	1	1	1	0.885
Hematopoietic cell lineage	0.741	0.766	0.809	0.759	0.791	0.717	0.727
Hepatitis C	0.534	0.553	0.565	0.583	0.62	0.642	0.612
Huntingtons disease	0.717	0.67	0.72	0.745	0.741	0.717	0.796
Hypertrophic cardiomyopathy (HCM)	0.62	0.598	0.625	0.574	0.691	0.644	0.657
Inositol phosphate metabolism	0.587	0.633	0.543	0.632	0.573	0.566	0.578
Intestinal immune network for IgA production	0.759	0.764	0.906	0.902	0.898	0.813	0.79
Jak-STAT signaling pathway	0.681	0.703	0.617	0.641	0.674	0.619	0.577
Leishmaniasis	0.69	0.696	0.676	0.685	0.708	0.68	0.733
Leukocyte transendothelial migration	0.603	0.628	0.64	0.656	0.622	0.576	0.794
Long-term depression	0.553	0.553	0.586	0.567	0.727	0.616	0.539
Long-term potentiation	0.55	0.607	0.559	0.561	0.598	0.663	0.569
Lysine degradation	0.73	0.646	0.607	0.626	0.587	0.641	0.619
Lysosome	0.539	0.541	0.585	0.567	0.528	0.516	0.637
Malaria	0.769	0.73	0.774	0.798	0.822	0.806	0.825
Melanogenesis	0.605	0.643	0.674	0.686	0.607	0.608	0.59
Melanoma	0.53	0.533	0.596	0.599	0.547	0.642	0.643
Metabolic pathways	0.589	0.592	0.602	0.593	0.603	0.598	0.627
Metabolism of xenobiotics by cytochrome P450	0.754	0.731	0.787	0.79	0.81	0.758	0.682
Mismatch repair	0.73	0.721	0.755	0.741	0.748	0.746	0.862
mRNA surveillance pathway	0.566	0.56	0.511	0.567	0.636	0.576	0.75
mTOR signaling pathway	0.626	0.577	0.611	0.58	0.596	0.578	0.705
N-Glycan biosynthesis	0.749	0.678	0.761	0.797	0.772	0.62	0.702
Natural killer cell mediated cytotoxicity	0.607	0.53	0.644	0.573	0.563	0.577	0.788
NOD-like receptor signaling pathway	0.603	0.562	0.624	0.648	0.553	0.658	0.603
Non-small cell lung cancer	0.613	0.574	0.522	0.55	0.55	0.568	0.6
Notch signaling pathway	0.578	0.594	0.659	0.55	0.554	0.608	0.749
Nucleotide excision repair	0.61	0.589	0.588	0.665	0.689	0.665	0.743
Oocyte meiosis	0.628	0.613	0.606	0.643	0.622	0.656	0.537
Osteoclast differentiation	0.646	0.675	0.625	0.573	0.652	0.588	0.688
Oxidative phosphorylation	0.79	0.696	0.78	0.799	0.8	0.788	0.877
p53 signaling pathway	0.532	0.638	0.636	0.59	0.617	0.612	0.575
Pancreatic cancer	0.611	0.543	0.594	0.612	0.624	0.619	0.544
Pancreatic secretion	0.607	0.574	0.604	0.6	0.575	0.573	0.629
Parkinsons disease	0.782	0.712	0.781	0.775	0.771	0.784	0.883
Pathogenic Escherichia coli infection	0.602	0.647	0.658	0.62	0.598	0.566	0.686
Pentose phosphate pathway	0.64	0.669	0.682	0.623	0.628	0.728	0.705
Peroxisome	0.546	0.559	0.659	0.611	0.703	0.534	0.598
Phagosome	0.7	0.703	0.699	0.712	0.77	0.703	0.713
PPAR signaling pathway	0.558	0.651	0.629	0.611	0.618	0.572	0.572
Prion diseases	0.687	0.641	0.637	0.67	0.63	0.67	0.76
Progesterone-mediated oocyte maturation	0.611	0.635	0.629	0.582	0.614	0.61	0.637
Propanoate metabolism	0.782	0.626	0.702	0.642	0.629	0.753	0.681
Prostate cancer	0.511	0.587	0.582	0.584	0.532	0.571	0.614
Proteasome	0.86	0.794	0.86	0.848	0.816	0.857	0.808
Protein digestion and absorption	0.89	0.897	0.886	0.88	0.867	0.847	0.868
Protein processing in endoplasmic reticulum	0.722	0.698	0.742	0.742	0.727	0.722	0.633
Purine metabolism	0.607	0.594	0.575	0.621	0.549	0.574	0.619
Pyrimidine metabolism	0.545	0.534	0.552	0.599	0.61	0.571	0.633
Pyruvate metabolism	0.643	0.584	0.643	0.609	0.611	0.722	0.603
Regulation of actin cytoskeleton	0.636	0.616	0.63	0.621	0.621	0.618	0.652
Retinol metabolism	0.768	0.733	0.727	0.631	0.617	0.685	0.756
Rheumatoid arthritis	0.698	0.639	0.72	0.699	0.71	0.717	0.663
Ribosome	0.927	0.835	0.891	0.898	0.91	0.914	0.924
Ribosome biogenesis in eukaryotes	0.682	0.722	0.753	0.737	0.649	0.744	0.805
RIG-I-like receptor signaling pathway	0.562	0.475	0.625	0.598	0.652	0.65	0.627
RNA degradation	0.611	0.628	0.597	0.626	0.665	0.658	0.789
RNA polymerase	0.683	0.685	0.634	0.605	0.607	0.701	0.8
RNA transport	0.669	0.666	0.669	0.668	0.682	0.679	0.752
Salivary secretion	0.658	0.568	0.606	0.524	0.659	0.623	0.668
Shigellosis	0.574	0.596	0.589	0.592	0.639	0.545	0.673
Small cell lung cancer	0.541	0.6	0.556	0.549	0.574	0.606	0.67
SNARE interactions in vesicular transport	0.697	0.669	0.606	0.684	0.651	0.655	0.773
Sphingolipid metabolism	0.564	0.634	0.686	0.601	0.661	0.67	0.629
Spliceosome	0.749	0.743	0.76	0.78	0.733	0.754	0.763
Staphylococcus aureus infection	0.889	0.878	0.896	0.894	0.849	0.9	0.839
Starch and sucrose metabolism	0.643	0.656	0.676	0.692	0.634	0.602	0.65
Systemic lupus erythematosus	0.812	0.78	0.832	0.816	0.803	0.803	0.84
T cell receptor signaling pathway	0.542	0.582	0.55	0.57	0.577	0.594	0.606
Terpenoid backbone biosynthesis	0.64	0.672	0.734	0.73	0.655	0.69	0.84
TGF-beta signaling pathway	0.619	0.617	0.591	0.637	0.601	0.64	0.587
Tight junction	0.578	0.583	0.572	0.601	0.612	0.623	0.538
Toll-like receptor signaling pathway	0.585	0.575	0.58	0.529	0.596	0.561	0.665
Toxoplasmosis	0.581	0.594	0.547	0.603	0.565	0.599	0.626
Tryptophan metabolism	0.591	0.704	0.696	0.684	0.685	0.589	0.646
Type I diabetes mellitus	0.868	0.905	0.871	0.913	0.849	0.917	0.729
Tyrosine metabolism	0.762	0.768	0.748	0.742	0.717	0.711	0.61
Ubiquitin mediated proteolysis	0.691	0.664	0.634	0.676	0.649	0.622	0.608
Valine, leucine and isoleucine degradation	0.671	0.543	0.766	0.699	0.689	0.69	0.772
Vascular smooth muscle contraction	0.53	0.546	0.599	0.578	0.624	0.572	0.613
Vasopressin-regulated water reabsorption	0.639	0.681	0.529	0.605	0.613	0.623	0.634
VEGF signaling pathway	0.591	0.562	0.592	0.534	0.541	0.504	0.656
Vibrio cholerae infection	0.609	0.516	0.629	0.565	0.586	0.623	0.802
Viral myocarditis	0.698	0.71	0.742	0.764	0.747	0.745	0.673
Wnt signaling pathway	0.665	0.626	0.576	0.59	0.598	0.714	0.609

d1d2d3d4d5d6

6.3 Sample class prediction

For each data table, machine learning models are built to predict sample class:LumA,LumB. In OmicsEV, random forest models are built and the models are evaluated using repeated 5 fold cross validation (20 times).

dataSet	mean_ROC	median_ROC	sd_ROC
d1	0.9900	0.9910	0.0054
d2	0.9943	0.9952	0.0029
d3	0.9887	0.9880	0.0035
d4	0.9898	0.9904	0.0031
d5	0.9913	0.9922	0.0033
d6	0.9940	0.9952	0.0026
Protein	0.7949	0.7972	0.0136

6.4 PCA with sample class annotation

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6.5 Unsupervised clustering

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7 Multi-omics concordance

7.1 Gene-wise mRNA-protein correlation

data table	n	n5	n6	n7	n8	gene_wise_cor
d1	9120	1911	773	210	21	0.3294
d2	9120	1970	827	222	23	0.3354
d3	9120	2010	827	227	25	0.3374
d4	9120	2016	837	224	27	0.3384
d5	9120	1763	696	185	20	0.3207
d6	9120	1932	763	205	20	0.3283

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7.2 Sample-wise mRNA-protein correlation

data table	sample_wise_cor
d1	0.1421
d2	0.1421
d3	0.1421
d4	0.1421
d5	0.1421
d6	0.1383